Quick start¶

To demonstrate how PyRCS works, this part of the documentation provides a quick guide with examples of getting location codes, Engineer’s Line References (ELRs) and railway stations data.

Get location codes¶

The location codes (including CRS, NLC, TIPLOC and STANOX) are categorised as line data. Import the class LocationIdentifiers() as follows:

>>> from pyrcs.line_data import LocationIdentifiers

>>> # Or,
>>> # from pyrcs import LocationIdentifiers

Now we can create an instance for getting the location codes:

>>> lid = LocationIdentifiers()

Note

An alternative way of creating the instance is through the class LineData() (see below).

>>> from pyrcs import LineData

>>> ld = LineData()
>>> lid_ = ld.LocationIdentifiers

Note

The instance ld contains all classes under the category of line data. Here lid_ is equivalent to lid.

Get location codes for a given initial letter¶

By using the method LocationIdentifiers.collect_loc_codes_by_initial(), we can get the location codes that start with a specific letter, say 'A' or 'a':

>>> # The input is case-insensitive
>>> loc_codes_a = lid.collect_loc_codes_by_initial('A')

>>> type(loc_codes_a)
dict
>>> list(loc_codes_a.keys())
['A', 'Additional notes', 'Last updated date']

loc_codes_a is a dictionary (i.e. dict type), with the following keys:

'A'
'Additional notes'
'Last updated date'

Their corresponding values are

loc_codes_a['A']: a data frame (in pandas.DataFrame type) of the location names that begin with ‘A’. We may compare it with the table on the web page of Locations beginning with ‘A’;
loc_codes_a['Additional notes']: some additional information on the web page (if available);
loc_codes_a['Last updated date']: the date when the web page was last updated.

Below is a snapshot of the codes of the location names beginning with ‘A’:

>>> loc_codes_a['A'].head()
                               Location CRS  ... STANME_Note STANOX_Note
0                                Aachen      ...
1                    Abbeyhill Junction      ...
2                 Abbeyhill Signal E811      ...
3            Abbeyhill Turnback Sidings      ...
4  Abbey Level Crossing (Staffordshire)      ...
[5 rows x 12 columns]

>>> print("Last updated date: {}".format(loc_codes_a['Last updated date']))
Last updated date: 2021-03-21

Get all available location codes¶

To get all available location codes in this category, use the method LocationIdentifiers.fetch_location_codes():

>>> loc_codes = lid.fetch_location_codes()

>>> type(loc_codes)
dict
>>> list(loc_codes.keys())
['Location codes', 'Other systems', 'Additional notes', 'Last updated date']

loc_codes is also a dictionary, of which the keys are as follows:

'Location codes'
'Other systems'
'Additional notes'
'Latest update date'

Their corresponding values are

loc_codes['Location codes']: a pandas.DataFrame of all location codes (from ‘A’ to ‘Z’);
loc_codes['Other systems']: a dictionary for other systems;
loc_codes['Additional notes']: some additional information on the web page (if available);
loc_codes['Latest update date']: the latest 'Last updated date' among all initial letter-specific codes.

Below is a snapshot of a random sample of the location codes data:

>>> loc_codes[lid.Key].head(10)
                               Location  CRS  ... STANME_Note STANOX_Note
                              Aachen       ...
                  Abbeyhill Junction       ...
               Abbeyhill Signal E811       ...
          Abbeyhill Turnback Sidings       ...
Abbey Level Crossing (Staffordshire)       ...
                      Abbey Road DLR  ZAL  ...
                          Abbey Wood  ABW  ...
     Abbey Wood Alsike Road Junction       ...
                Abbey Wood Crossrail  ABX  ...
         Abbey Wood Crossrail Siding       ...
[10 rows x 12 columns]

Get ELRs and mileages¶

To get ELRs and mileages, use the class ELRMileages():

>>> from pyrcs.line_data import ELRMileages
>>> # Or simply
>>> # from pyrcs import ELRMileages

>>> em = ELRMileages()

Get ELR codes¶

To get ELR codes which start with 'A', use the method ELRMileages.collect_elr_by_initial(), which returns a dictionary:

>>> elrs_a = em.collect_elr_by_initial('A')

>>> type(elrs_a)
dict
>>> list(elrs_a.keys())
['A', 'Last updated date']

The keys of the dictionary elrs_a include:

'A'
'Last updated date'

Their corresponding values are

elrs_a['A']: a data frame of ELRs that begin with ‘A’. We may compare it with the table on the web page of ELRs beginning with ‘A’;
elrs_a['Last updated date']: the date when the web page was last updated.

Below is a snapshot of the data of the ELR codes beginning with ‘A’:

>>> elrs_a['A'].head()
   ELR  ...         Notes
0  AAL  ...      Now NAJ3
1  AAM  ...  Formerly AML
2  AAV  ...
3  ABB  ...       Now AHB
4  ABB  ...
[5 rows x 5 columns]

>>> print("Last updated date: {}".format(elrs_a['Last updated date']))
Last updated date: 2020-10-27

To get all available ELR codes, use the method ELRMileages.fetch_elr(), which also returns a dictionary:

>>> elrs_dat = em.fetch_elr()

>>> type(elrs_dat)
dict
>>> list(elrs_dat.keys())
['ELRs', 'Last updated date']

The keys of elrs_dat include:

'ELRs'
'Latest update date'

Their corresponding values are

elrs_dat['ELRs']: a pandas.DataFrame of all available ELRs (from ‘A’ to ‘Z’);
elrs_dat['Latest update date']: the latest 'Last updated date' among all initial letter-specific codes.

Below is a snapshot of a random sample of the ELR codes data:

>>> elrs_dat[em.Key].head()
    ELR  ...                   Notes
 AAL  ...                Now NAJ3
 AAM  ...            Formerly AML
 AAV  ...
 ABB  ...                 Now AHB
 ABB  ...
 ABD  ...
 ABE  ...  Formerly ABE1 and ABE2
 ABE  ...
ABE1  ...         Now part of ABE
ABE2  ...         Now part of ABE
[10 rows x 5 columns]

Get mileage data for a given ELR¶

To get detailed mileage data for a given ELR, for example, AAM, use the method ELRMileages.fetch_mileage_file(), which returns a dictionary as well:

>>> em_amm = em.fetch_mileage_file('AAM')

>>> type(em_amm)
dict
>>> list(em_amm.keys())
['ELR', 'Line', 'Sub-Line', 'Mileage', 'Notes']

The keys of em_amm include:

'ELR'
'Line'
'Sub-Line'
'Mileage'
'Notes'

Their corresponding values are

em_amm['ELR']: the name of the given ELR (which in this example is ‘AAM’);
em_amm['Line']: the associated line name;
em_amm['Sub-Line']: the associated sub line name (if available);
em_amm['Mileage']: a pandas.DataFrame of the mileage file data;
em_amm['Notes']: additional information/notes (if any).

Below is a snapshot of the mileage data of AAM:

>>> em_amm['Mileage'].head(10)
   Mileage Mileage_Note  ... Link_2_ELR Link_2_Mile_Chain
 0.0000               ...
 0.0154               ...
 0.0396               ...
 1.1012               ...
 1.1408               ...
 5.0330               ...
 7.0374               ...
11.1298               ...
13.0638               ...
[9 rows x 11 columns]

Get railway stations data¶

The railway station data (incl. the station name, ELR, mileage, status, owner, operator, degrees of longitude and latitude, and grid reference) is categorised into other assets in the source data.

>>> from pyrcs.other_assets import Stations
>>> # Or simply
>>> # from pyrcs import Stations

>>> stn = Stations()

Note

Alternatively, the instance stn can also be defined through OtherAssets() that contains all classes under the category of other assets (see below).

>>> from pyrcs import OtherAssets

>>> oa = OtherAssets()
>>> stn_ = oa.Stations

Note

stn_ is equivalent to stn.

To get the data of railway stations whose names start with a specific letter, e.g. 'A', use the method Stations.collect_station_data_by_initial():

>>> stn_data_a = stn.collect_station_data_by_initial('A')

>>> type(stn_data_a)
dict
>>> list(stn_data_a.keys())
['A', 'Last updated date']

The keys of stn_data_a include:

'A'
'Last updated date'

The corresponding values are

stn_data_a['A']: a pandas.DataFrame of the data of railway stations whose names begin with ‘A’. We may compare it with the table on the web page of Stations beginning with ‘A’;
stn_data_a['Last updated date']: the date when the web page was last updated.

Below is a snapshot of the data of the railway stations beginning with ‘A’:

>>> stn_data_a['A'].head()
      Station   ELR  ... Prev_Operator_6 Prev_Operator_Period_6
0  Abbey Wood   NKL  ...
1  Abbey Wood  XRS3  ...
2        Aber   CAR  ...
3   Abercynon   CAM  ...
4   Abercynon   ABD  ...
[5 rows x 28 columns]

>>> print("Last updated date: {}".format(stn_data_a['Last updated date']))
Last updated date: 2021-02-22

To get available railway station data (from ‘A’ to ‘Z’) in this category, use the method Stations.fetch_station_data()

>>> stn_data = stn.fetch_station_data()

>>> type(stn_data)
dict
>>> list(stn_data.keys())
['Mileages, operators and grid coordinates', 'Last updated date']

The keys of stn_data include:

'Mileages, operators and grid coordinates'
'Latest update date'

Their corresponding values are

stn_data['Mileages, operators and grid coordinates']: a pandas.DataFrame of available railway station data (from ‘A’ to ‘Z’);
stn_data['Latest update date']: the latest 'Last updated date' among all initial letter-specific codes.

Below is a snapshot of a random sample of the railway station data:

>>> stn_data[stn.StnKey].head(10)
           Station   ELR  ... Prev_Operator_6 Prev_Operator_Period_6
0       Abbey Wood  XRS3  ...
1       Abbey Wood   NKL  ...
2             Aber   CAR  ...
3        Abercynon   ABD  ...
4        Abercynon   CAM  ...
5  Abercynon North   ABD  ...
6         Aberdare   VON  ...
7         Aberdeen  ANI1  ...
8         Aberdeen  ECN5  ...
9         Aberdour  ECN2  ...
[10 rows x 30 columns]

>>> print("Last updated date: {}".format(stn_data['Last updated date']))
Last updated date: 2021-03-21

(The end of the quick start)

For more details and examples, check Sub-packages and modules.